A UAV-Mounted Sensor Network for Close-Range Inspection of Wind Turbine Rotor Blades
This work addresses the need for automated defect classification in offshore wind turbine blade inspection, but results are preliminary and limited to lab settings.
The paper presents a UAV-mounted multimodal sensor network for close-range inspection of wind turbine rotor blades, combining RGB, thermal, and 3D sensors. Preliminary lab results show synchronized multi-sensor acquisition and initial point cloud reconstructions.
Inspection of offshore wind turbine rotor blades is critical for predictive maintenance to maximise efficiency and extend operational lifetime. However, it remains a challenging task due to remote locations, large structural dimensions, and the limitations of current UAV-compatible sensor systems. While existing approaches can detect certain types of surface anomalies, reliable classification of defect types often remains a manual and error-prone process. This paper presents the design of a UAV-mounted multimodal sensor network combining an industrial RGB camera, a passive thermal infrared camera, and an in-house developed 3D scanner. All sensors are co-calibrated into a common coordinate frame, enabling spatial superimposition of geometric, colour, and thermal data. The system is designed to operate at close range, addressing three fundamental sensing challenges: platform motion, large field of view, and millimetre-level measurement accuracy. Preliminary laboratory results demonstrate synchronised multi-sensor acquisition and initial point cloud reconstructions, forming the basis for future airborne inspection trials.